Energy Vulnerability Principal Components for LSOA in England
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
For a case study of England, global principal component analysis (PCA) is applied to a suite of neighborhood-scale energy vulnerability indicators.
PCA reduces a large multivariate set of vulnerability factors into a reduced number of principal components, retaining key statistical information and spatial patterns. The components have loading values associated with each of the vulnerability indicators in the input data set. Loadings tell us about the type (negative or positive) and strength of the relationship between an indicator and a principal component, providing information about the patterns of vulnerability within the data set that each component is likely to represent. These global component loadings can be mapped to provide an understanding of the spatial distribution of the vulnerability represented by each principal component and the locales in which vulnerability is likely to be enhanced as a result.
This dataset contains three principal components which account for 62.4 percent of the variance in the 21 energy vulnerability indicators identified. The first component has strong positive association with precarious and transient families but a strong inverse relationship with retirement and older age groups. The second component has a strong positive relationship with disability, illness, and the provision of care. The third component has a positive relationship with the energy efficiency and availability of networked and domestic energy infrastructures. The principal components are mapped at the Lower Super Output Area (LSOA) scale, an administrative area unit with a mean population of 1,500 persons.